Updog.link – a URL shortener you can run for free

Hacker News

Hi HN. I half-built a dumb link-shortening service in 2020 on API Gateway and DynamoDB, and never finished it, but that didn't stop me from registering an 'updog'-themed domain for it. I forgot about it until tonight and decided it was worth finishing this tool from a bygone era. I scrapped everything and moved to more modern tools: one script on a Cloudflare Worker, with the links (and the Terraform state) in R2. You can use what I'm hosting if you want, or clone it and deploy your own. Here are some of the characteristics: It's free-ish. It runs on Cloudflare's free tier, which serves something like a hundred thousand requests a day. It's stateless-ish. Each link is an immutable JSON object in R2. No traditional databases or servers, nothing to really run or maintain. It's private-ish. There are no click stats, so nobody can see who followed a link, including the person who made it. Cloudflare's edge presumably logs something somewhere, because that's what edges do. It's funny-ish, if you can remember being 12. What surprised me most is how simple a task this has become. Not only is writing code much faster, but the architecture and footprint are much smaller. I like to think I'm also better at my job, but that part is probably negligible. The S3-ish object + short-lived compute architecture was sorta possible in 2020, but not that common as I recall. The simplicity is what let me rework and publish it from the couch in an evening. You can make a shortlink with it here: https://whats.updog.link Source (MIT): https://github.com/ignore-prior-instructions/Updog.link

Visit product View on Hacker NewsSep 11, 2026

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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